---
title: "HCAMP Cluster Comparison"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
vertical_layout: scroll
theme: united
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(here)
library(janitor)
library(rio)
library(colorblindr)
library(gghighlight)
library(forcats)
library(ggrepel)
library(knitr)
library(kableExtra)
library(reactable)
library(plotly)
library(glue)
library(fs)
library(rstatix)
library(ggpubr)
library(writexl)
library(remotes)
library(profvis)
theme_fivethirtyeight <- function(base_size = 15, base_family = "") {
theme_grey(base_size = base_size, base_family = base_family) %+replace%
theme(
# Base elements which are not used directly but inherited by others
line = element_line(colour = '#DADADA', size = 0.75,
linetype = 1, lineend = "butt"),
rect = element_rect(fill = "#F0F0F0", colour = "#F0F0F0",
size = 0.5, linetype = 1),
text = element_text(family = base_family, face = "plain",
colour = "#656565", size = base_size,
hjust = 0.5, vjust = 0.5, angle = 0,
lineheight = 0.9),
# Modified inheritance structure of text element
plot.title = element_text(size = rel(1.5), family = '' ,
face = 'bold', hjust = -0.05,
vjust = 1.5, colour = '#3B3B3B'),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text = element_text(),
# Modified inheritance structure of line element
axis.ticks = element_line(),
panel.grid.major = element_line(),
panel.grid.minor = element_blank(),
# Modified inheritance structure of rect element
plot.background = element_rect(),
panel.background = element_rect(),
legend.key = element_rect(colour = '#DADADA'),
# Modifiying legend.position
legend.position = 'none',
complete = TRUE
)
}
theme_set(theme_fivethirtyeight())
# theme_set(theme_minimal(15) +
# theme(legend.position = "bottom",
# panel.grid.major.x = element_line(color = "gray60"),
# panel.grid.minor.x = element_blank(),
# panel.grid.major.y = element_blank())
# )
```
```{r global, include=FALSE}
#all clean sims data
sims_concussion_data <- read_csv(here("data", "sims_concussion_data.csv"))
sims_concussion_data <- sims_concussion_data %>%
mutate(age = as.factor(age))
#impact data only
one_i_test <- read_csv(here("data", "one_impact_pcss_test.csv"))
one_impact_test <- one_i_test %>%
select(-c(5, 6))
two_i_test <- read_csv(here("data", "two_impact_pcss_tests.csv"))
two_impact_test <- two_i_test %>%
select(-c(6, 7))
three_i_test <- read_csv(here("data", "three_impact_pcss_tests.csv"))
three_impact_test <- three_i_test %>%
select(-c(7, 8))
four_i_test <- read_csv(here("data", "four_impact_pcss_tests.csv"))
four_impact_test <- four_i_test %>%
select(-c(8, 9))
# impact sims merge data
one_impact_sims <- read_csv(here("data", "one_post_injury_test_impact_sims_merge.csv"))
two_impact_sims <- read_csv(here("data", "two_post_injury_test_impact_sims_merge.csv"))
three_impact_sims <- read_csv(here("data", "three_post_injury_test_impact_sims_merge.csv"))
four_impact_sims <- read_csv(here("data", "four_post_injury_test_impact_sims_merge.csv"))
baseline_dates <- read_csv(here("data", "baseline_dates.csv"))
# pcss items
pcss_items <- import(here("data", "pcss_items.xlsx"),
setclass = "tbl_df")
pcss_items_data <- import(here("data", "pcss_items_data.xlsx"),
setclass = "tbl_df")
pcss_cluster <- import(here("data", "cluster_scores.xlsx"),
setclass = "tbl_df")
```
# Symptom Comparisons
Sidebar {.sidebar}
------------
Symptoms that appear to be rated with higher severity more consistently:
1. Headache
2. Difficulty concentrating
3. Difficulty remembering
4. Irritability
5. Light sensitivity
Symptoms from the headache-migraine, cognitive and anxiety-mood clusters appear to remain elevated throughout recovery.
The vestibular cluster symptom of dizziness appears to be rated highest at the time of first testing and lessen in severity across the recovery time.
```{r, include=FALSE}
one_impact_test_cluster <- one_impact_test %>%
pivot_longer(
cols = c(32:37),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
) %>%
select(c(1:8, 37, 38))
one_impact_test_symptoms <- one_impact_test %>%
pivot_longer(
cols = c(9:30),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(c(1:15, 21, 22))
tst_aov <- aov(total_symptom_score_post_injury_1 ~ post_injury_1_rating,
data = one_impact_test_symptoms)
summary(tst_aov)
one_impact_test_symptoms <- left_join(one_impact_test_symptoms, pcss_items_data)
```
```{r, include=FALSE}
two_impact_test_1 <- two_impact_test %>%
select(passport_id, gender, contains("_post_injury_1"))
two_impact_test_2 <- two_impact_test %>%
select(passport_id, gender, contains("_post_injury_2"))
two_impact_test_1_symptoms <- two_impact_test_1 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(-c(14:18))
two_impact_test_1_symptoms <- left_join(two_impact_test_1_symptoms, pcss_items_data)
two_impact_test_2_symptoms <- two_impact_test_2 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_2_rating",
names_pattern = "(.*)_post_injury_2"
) %>%
select(-c(14:18))
two_impact_test_2_symptoms <- left_join(two_impact_test_2_symptoms, pcss_items_data)
```
```{r, include=FALSE}
three_impact_test_1 <- three_impact_test %>%
select(passport_id, gender, contains("_post_injury_1"))
three_impact_test_2 <- three_impact_test %>%
select(passport_id, gender, contains("_post_injury_2"))
three_impact_test_3 <- three_impact_test %>%
select(passport_id, gender, contains("_post_injury_3"))
three_impact_test_1_symptoms <- three_impact_test_1 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(-c(14:18))
three_impact_test_1_symptoms <- left_join(three_impact_test_1_symptoms, pcss_items_data)
three_impact_test_2_symptoms <- three_impact_test_2 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_2_rating",
names_pattern = "(.*)_post_injury_2"
) %>%
select(-c(14:18))
three_impact_test_2_symptoms <- left_join(three_impact_test_2_symptoms, pcss_items_data)
three_impact_test_3_symptoms <- three_impact_test_3 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_3_rating",
names_pattern = "(.*)_post_injury_3"
) %>%
select(-c(14:18))
three_impact_test_3_symptoms <- left_join(three_impact_test_3_symptoms, pcss_items_data)
```
```{r, include=FALSE}
four_impact_test_1 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_1"))
four_impact_test_2 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_2"))
four_impact_test_3 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_3"))
four_impact_test_4 <- four_impact_test %>%
select(passport_id, gender, contains("_post_injury_4"))
four_impact_test_1_symptoms <- four_impact_test_1 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_1_rating",
names_pattern = "(.*)_post_injury_1"
) %>%
select(-c(14:18))
four_impact_test_1_symptoms <- left_join(four_impact_test_1_symptoms, pcss_items_data)
four_impact_test_2_symptoms <- four_impact_test_2 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_2_rating",
names_pattern = "(.*)_post_injury_2"
) %>%
select(-c(14:18))
four_impact_test_2_symptoms <- left_join(four_impact_test_2_symptoms, pcss_items_data)
four_impact_test_3_symptoms <- four_impact_test_3 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_3_rating",
names_pattern = "(.*)_post_injury_3"
) %>%
select(-c(14:18))
four_impact_test_3_symptoms <- left_join(four_impact_test_3_symptoms, pcss_items_data)
four_impact_test_4_symptoms <- four_impact_test_4 %>%
pivot_longer(
cols = c(7:28),
names_to = "symptom",
names_prefix = "pcss_",
values_to = "post_injury_4_rating",
names_pattern = "(.*)_post_injury_4"
) %>%
select(-c(14:18))
four_impact_test_4_symptoms <- left_join(four_impact_test_4_symptoms, pcss_items_data)
```
Row {.tabset}
-----------------------------------------------------------------------
### One Test
```{r, include=FALSE}
all_symptom_tbl <- one_impact_test_symptoms %>%
group_by(gender, cluster, symptom) %>%
summarize(Mean = mean(post_injury_1_rating),
SD = sd(post_injury_1_rating),
Min = min(post_injury_1_rating),
Max = max(post_injury_1_rating),
Total = length(post_injury_1_rating)) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
cluster = colDef(name = "Symptom Cluster"),
symptom = colDef(name = "Symptom"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
symptom_tbl <- function(df, a, b, c, d) {
df %>%
group_by({{a}}, {{b}}, {{c}}) %>%
summarize(Mean = mean({{d}}),
SD = sd({{d}}),
Min = min({{d}}),
Max = max({{d}}),
Total = length({{d}})) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
cluster = colDef(name = "Symptom Cluster"),
symptom = colDef(name = "Symptom"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
}
symptom_tbl_over_three <- function(df, a, b, c, d) {
df %>%
group_by({{a}}, {{b}}, {{c}}) %>%
filter({{d}} >=3) %>%
summarize(Mean = mean({{d}}),
SD = sd({{d}}),
Min = min({{d}}),
Max = max({{d}}),
Total = length({{d}})) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
cluster = colDef(name = "Symptom Cluster"),
symptom = colDef(name = "Symptom"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE)),
Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
}
```
```{r, include=TRUE}
symptom_tbl(one_impact_test_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
Row {.tabset}
-----------------------------------------------------------------------
### Two Tests - Test 1
```{r, include=TRUE}
symptom_tbl(two_impact_test_1_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
### Two Tests - Test 2
```{r, include=TRUE}
symptom_tbl(two_impact_test_2_symptoms, gender, cluster, symptom, post_injury_2_rating)
```
Row {.tabset}
-----------------------------------------------------------------------
### Three Tests - Test 1
```{r, include=TRUE}
symptom_tbl(three_impact_test_1_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
### Three Tests - Test 2
```{r, include=TRUE}
symptom_tbl(three_impact_test_2_symptoms, gender, cluster, symptom, post_injury_2_rating)
```
### Three Tests - Test 3
```{r, include=TRUE}
symptom_tbl(three_impact_test_3_symptoms, gender, cluster, symptom, post_injury_3_rating)
```
Row {.tabset}
-----------------------------------------------------------------------
### Four Tests - Test 1
```{r, include=TRUE}
symptom_tbl(four_impact_test_1_symptoms, gender, cluster, symptom, post_injury_1_rating)
```
### Four Tests - Test 2
```{r, include=TRUE}
symptom_tbl(four_impact_test_2_symptoms, gender, cluster, symptom, post_injury_2_rating)
```
### Four Tests - Test 3
```{r, include=TRUE}
symptom_tbl(four_impact_test_3_symptoms, gender, cluster, symptom, post_injury_3_rating)
```
### Four Tests - Test 4
```{r, include=TRUE}
symptom_tbl(four_impact_test_4_symptoms, gender, cluster, symptom, post_injury_4_rating)
```
# Cluster Comparisons
Sidebar {.sidebar}
------------
Symptom Cluster Potential Symptom Severity Range:
1. Headache-Migraine Cluster: 0 - 24 (4 symptoms)
2. Cognitive Cluster: 0 - 24 (4 symptoms)
3. Anxiety-Mood Cluster: 0 - 24 (4 symptoms)
4. Ocular-Motor Cluster: 0 - 6 (1 symptom)
5. Vestibular Cluster: 0 - 24 (4 symptoms)
6. Sleep Cluster: 0 - 30 (5 symptoms)
To compare symptom cluster severity scores, each cluster was rescaled for the distribution of scores to fall between 0 to 1. Each cluster was rescaled individually and then grouped together into the same variable corresponding to post-injury test to compare with one-way ANOVAs.
**One Test Summary**
* Non-significant cluster comparisons: anxiety-mood/ocular-motor and cognitive/sleep
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
* Cognitive and sleep clusters tied for second highest rated clusters
* Anxiety-mood and ocular-motor rated the lowest
**Two Tests Summary**
_Test One:_
* Non-significant cluster comparisons: cognitive/sleep
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
* Cognitive and sleep clusters tied for second highest rated clusters
* Anxiety-mood and ocular-motor rated the lowest
_Test Two:_
* Non-significant cluster comparisons: anxiety-mood/ocular-motor, cognitive/sleep, anxiety-mood/vestibular, and ocular-motor/vestibular
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
**Three Tests Summary**
_Test One:_
* Non-significant cluster comparisons: anxiety-mood/vestibular
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
_Test Two:_
* Non-significant cluster comparisons: anxiety-mood/ocular-motor, cognitive/sleep, anxiety-mood/vestibular, and ocular-motor/vestibular
* Headache-migraine cluster significantly higher than all other clusters
_Test Three:_
* Non-significant cluster comparisons: cognitive/headache-migraine, anxiety-mood/ocular-motor, cognitive/sleep, headache-migraine/sleep, anxiety-mood/vestibular, and ocular-motor/vestibular
* Headache-migraine, cognitive, and sleep clusters significantly higher than other clusters
**Four Tests Summary**
_Test One:_
* Non-significant cluster comparisons: anxiety-mood/ocular-motor, cognitive/sleep, cognitive/vestibular, and sleep/vestibular
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
* Cognitive, vestibular, and sleep clusters not significantly different than each other and represent second set of clusters behind headache-migraine
_Test Two:_
* Non-significant cluster comparisons: anxiety-mood/ocular-motor, cognitive/sleep, and ocular-motor/vestibular
* All other PWC were significant
* Headache-migraine cluster significantly higher than all other clusters
_Test Three:_
* Non-significant cluster comparisons: cognitive/headache-migraine, anxiety-mood/ocular-motor, cognitive/sleep, anxiety-mood/vestibular, and ocular-motor/vestibular
* All other PWC were significant
* Headache-migraine cluster rated the highest but not significantly different than cognitive cluster
* Cognitive cluster rated second highest but not significantly different than sleep cluster
_Test Four:_
* Non-significant cluster comparisons: anxiety-mood/cognitive, cognitive/headache-migraine, anxiety-mood/ocular-motor, cognitive/ocular-motor, anxiety-mood/sleep, cognitive/sleep, headache-migraine/sleep, ocular-motor/sleep, anxiety-mood/vestibular, ocular-motor/vestibular, and sleep/vestibular
* Significant cluster comparisons: cognitive/vestibular, headache-migraine/vestibular, anxiety-mood/headache-migraine, ocular-motor/headache-migraine
* Probably most important comparison from this test was the significant difference between anxiety-mood and headache-migraine severity ratings
Row {.tabset}
-----------------------------------------------------------------------
### 1 Test Cluster Summary
```{r, include=FALSE}
clusters_one_impact_test <- one_impact_test %>%
select(c(1:8, 32:37)) %>%
mutate(#X1 = as.character(X1),
row = as.character(row),
age = as.character(age))
str(clusters_one_impact_test)
# normalize <- function(x) {
# return ((x - min(x)) / (max(x) - min(x)))
# }
rescale01b <- function(x) {
if(is.numeric(x)) {
z <- na.omit(x)
min_z <- min(z)
max_z <- max(z)
(z - min_z) / (max_z - min_z)
}
else {
return()
}
}
rescale01b(c(0, 5, 10))
clusters_one_impact_test_scaled <- map_df(clusters_one_impact_test, rescale01b)
clusters_one_impact_test_scaled <- bind_cols(clusters_one_impact_test,
clusters_one_impact_test_scaled)
clusters_one_impact_test_scaled <- clusters_one_impact_test_scaled %>%
select(-c(9:14))
clusters_one_impact_test_scaled[sapply(clusters_one_impact_test_scaled, is.character)] <- lapply(clusters_one_impact_test_scaled[sapply(clusters_one_impact_test_scaled, is.character)], as.factor)
str(clusters_one_impact_test_scaled)
clusters_one_impact_test_scaled_long <- clusters_one_impact_test_scaled %>%
pivot_longer(
cols = c(10:15),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
clusters_one_impact_test_long <- clusters_one_impact_test %>%
pivot_longer(
cols = c(9:14),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
str(clusters_one_impact_test_scaled_long)
```
```{r, include=FALSE}
#summary
cluster_smry_tbl <- function(df, x) {
df %>%
group_by(symptom_cluster) %>%
summarize(mean = mean({{x}}),
sd = sd({{x}}),
min = min({{x}}),
max = max({{x}}),
total = length({{x}})) %>%
mutate(mean = as.numeric(mean),
sd = as.numeric(sd),
min = as.numeric(min),
max = as.numeric(max),
total = as.numeric(total)) %>%
reactable(
columns = list(
symptom_cluster = colDef(name = "Symptom Cluster",
align = "center"),
mean = colDef(name = "Mean",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
sd = colDef(name = "SD",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
min = colDef(name = "Min",
align = "center",
format = colFormat(separators = TRUE)),
max = colDef(name = "Max",
align = "center",
format = colFormat(separators = TRUE)),
total = colDef(name = "Total",
align = "center",
format = colFormat(separators = TRUE, suffix = " concussions"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
}
cluster_smry_tbl(clusters_one_impact_test_scaled_long, post_injury_1_score)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_one_impact_test_scaled_long, post_injury_1_score)
```
### 1 Test Cluster Boxplot
```{r, include=FALSE}
cluster_bxp_1 <- ggplot(clusters_one_impact_test_scaled_long,
aes(symptom_cluster, post_injury_1_score)) +
geom_boxplot() +
labs(x = "Symptom Cluster",
y = "Scaled Severity Score") +
theme(legend.position = "none",
axis.text.x = element_text(angle = 25))
cluster_bxp <- function(df, x, y) {
ggplot(df, aes({{x}}, {{y}})) +
geom_boxplot() +
labs(x = "Symptom Cluster",
y = "Scaled Severity Score") +
theme(legend.position = "none",
axis.text.x = element_text(angle = 25))
}
cluster_bxp(clusters_one_impact_test_scaled_long, symptom_cluster, post_injury_1_score)
```
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_one_impact_test_scaled_long, symptom_cluster, post_injury_1_score))
```
### 1 Test Cluster ANOVA
```{r, include=FALSE}
cluster_one_test_aov <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_one_impact_test_scaled_long)
summary(cluster_one_test_aov)
cluster_one_test_aov
```
```{r, include=TRUE}
summary(cluster_one_test_aov)
```
### 1 Test Cluster PWC
```{r, include=FALSE}
cluster_one_test_aov_pwc <- clusters_one_impact_test_scaled_long %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_one_test_aov_pwc <- cluster_one_test_aov_pwc %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_one_test_aov_pwc %>%
reactable(pagination = TRUE,
striped = TRUE,
outlined = TRUE,
searchable = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE)
```
Row {.tabset}
-----------------------------------------------------------------------
### 2 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_two_impact_test_1 <- two_impact_test %>%
select(c(1:12, contains("_post_injury_1"))) %>%
mutate(#X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2))
clusters_two_impact_test_1 <- clusters_two_impact_test_1 %>%
select(c(1:12, 36:41))
clusters_two_impact_test_1_scaled <- map_df(clusters_two_impact_test_1, rescale01b)
clusters_two_impact_test_1_scaled <- bind_cols(clusters_two_impact_test_1,
clusters_two_impact_test_1_scaled)
clusters_two_impact_test_1_scaled <- clusters_two_impact_test_1_scaled %>%
select(-c(13:18))
clusters_two_impact_test_1_scaled[sapply(clusters_two_impact_test_1_scaled, is.character)] <- lapply(clusters_two_impact_test_1_scaled[sapply(clusters_two_impact_test_1_scaled, is.character)], as.factor)
str(clusters_two_impact_test_1_scaled)
clusters_two_impact_test_1_long <- clusters_two_impact_test_1_scaled %>%
pivot_longer(
cols = c(14:19),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
clusters_two_impact_test_2 <- two_impact_test %>%
select(c(1:12, contains("_post_injury_2"))) %>%
mutate(#X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2))
clusters_two_impact_test_2 <- clusters_two_impact_test_2 %>%
select(c(1:12, 36:41))
clusters_two_impact_test_2_scaled <- map_df(clusters_two_impact_test_2, rescale01b)
clusters_two_impact_test_2_scaled <- bind_cols(clusters_two_impact_test_2,
clusters_two_impact_test_2_scaled)
clusters_two_impact_test_2_scaled <- clusters_two_impact_test_2_scaled %>%
select(-c(13:18))
clusters_two_impact_test_2_scaled[sapply(clusters_two_impact_test_2_scaled, is.character)] <- lapply(clusters_two_impact_test_2_scaled[sapply(clusters_two_impact_test_2_scaled, is.character)], as.factor)
str(clusters_two_impact_test_2_scaled)
clusters_two_impact_test_2_long <- clusters_two_impact_test_2_scaled %>%
pivot_longer(
cols = c(14:19),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
clusters_two_impact_test_complete <- left_join(clusters_two_impact_test_1_long,
clusters_two_impact_test_2_long)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_two_impact_test_complete, post_injury_1_score)
```
### 2 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_two_impact_test_complete,
symptom_cluster, post_injury_1_score))
```
### 2 Tests Cluster ANOVA - Test 1
```{r, include=FALSE}
cluster_two_tests_aov_1 <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_two_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_two_tests_aov_1)
```
### 2 Tests Cluster PWC - Test 1
```{r, include=FALSE}
cluster_two_tests_aov_pwc_1 <- clusters_two_impact_test_complete %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_two_tests_aov_pwc_1 <- cluster_two_tests_aov_pwc_1 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_two_tests_aov_pwc_1 %>%
reactable(pagination = TRUE,
striped = TRUE,
outlined = TRUE,
searchable = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE)
```
### 2 Tests Cluster Summary - Test 2
```{r, include=TRUE}
cluster_smry_tbl(clusters_two_impact_test_complete, post_injury_2_score)
```
### 2 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_two_impact_test_complete,
symptom_cluster, post_injury_2_score))
```
### 2 Tests Cluster ANOVA - Test 2
```{r, include=FALSE}
cluster_two_tests_aov_2 <- aov(post_injury_2_score ~ symptom_cluster,
data = clusters_two_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_two_tests_aov_2)
```
### 2 Tests Cluster PWC - Test 2
```{r, include=FALSE}
cluster_two_tests_aov_pwc_2 <- clusters_two_impact_test_complete %>%
pairwise_t_test(
post_injury_2_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_two_tests_aov_pwc_2 <- cluster_two_tests_aov_pwc_2 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_two_tests_aov_pwc_2 %>%
reactable(pagination = TRUE,
striped = TRUE,
outlined = TRUE,
searchable = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE)
```
Row {.tabset}
-----------------------------------------------------------------------
### 3 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_three_impact_test_1 <- three_impact_test %>%
select(c(1:16, contains("_post_injury_1"))) %>%
mutate(#X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3))
clusters_three_impact_test_1 <- clusters_three_impact_test_1 %>%
select(c(1:16, 40:45))
clusters_three_impact_test_1_scaled <- map_df(clusters_three_impact_test_1, rescale01b)
clusters_three_impact_test_1_scaled <- bind_cols(clusters_three_impact_test_1,
clusters_three_impact_test_1_scaled)
clusters_three_impact_test_1_scaled <- clusters_three_impact_test_1_scaled %>%
select(-c(17:22))
clusters_three_impact_test_1_scaled[sapply(clusters_three_impact_test_1_scaled, is.character)] <- lapply(clusters_three_impact_test_1_scaled[sapply(clusters_three_impact_test_1_scaled, is.character)], as.factor)
str(clusters_three_impact_test_1_scaled)
clusters_three_impact_test_1_long <- clusters_three_impact_test_1_scaled %>%
pivot_longer(
cols = c(18:23),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
clusters_three_impact_test_2 <- three_impact_test %>%
select(c(1:16, contains("_post_injury_2"))) %>%
mutate(#X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3))
clusters_three_impact_test_2 <- clusters_three_impact_test_2 %>%
select(c(1:16, 40:45))
clusters_three_impact_test_2_scaled <- map_df(clusters_three_impact_test_2, rescale01b)
clusters_three_impact_test_2_scaled <- bind_cols(clusters_three_impact_test_2,
clusters_three_impact_test_2_scaled)
clusters_three_impact_test_2_scaled <- clusters_three_impact_test_2_scaled %>%
select(-c(17:22))
clusters_three_impact_test_2_scaled[sapply(clusters_three_impact_test_2_scaled, is.character)] <- lapply(clusters_three_impact_test_2_scaled[sapply(clusters_three_impact_test_2_scaled, is.character)], as.factor)
str(clusters_three_impact_test_2_scaled)
clusters_three_impact_test_2_long <- clusters_three_impact_test_2_scaled %>%
pivot_longer(
cols = c(18:23),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
clusters_three_impact_test_3 <- three_impact_test %>%
select(c(1:16, contains("_post_injury_3"))) %>%
mutate(#X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3))
clusters_three_impact_test_3 <- clusters_three_impact_test_3 %>%
select(c(1:16, 40:45))
clusters_three_impact_test_3_scaled <- map_df(clusters_three_impact_test_3, rescale01b)
clusters_three_impact_test_3_scaled <- bind_cols(clusters_three_impact_test_3,
clusters_three_impact_test_3_scaled)
clusters_three_impact_test_3_scaled <- clusters_three_impact_test_3_scaled %>%
select(-c(17:22))
clusters_three_impact_test_3_scaled[sapply(clusters_three_impact_test_3_scaled, is.character)] <- lapply(clusters_three_impact_test_3_scaled[sapply(clusters_three_impact_test_3_scaled, is.character)], as.factor)
str(clusters_three_impact_test_3_scaled)
clusters_three_impact_test_3_long <- clusters_three_impact_test_3_scaled %>%
pivot_longer(
cols = c(18:23),
names_to = "symptom_cluster",
values_to = "post_injury_3_score",
names_pattern = "(.*)_score_post_injury_3"
)
clusters_three_impact_test_join_1 <- left_join(clusters_three_impact_test_1_long,
clusters_three_impact_test_2_long)
clusters_three_impact_test_complete <- left_join(clusters_three_impact_test_join_1,
clusters_three_impact_test_3_long)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_three_impact_test_complete, post_injury_1_score)
```
### 3 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster, post_injury_1_score))
```
### 3 Tests Cluster ANOVA - Test 1
```{r, include=FALSE}
cluster_three_tests_aov_1 <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_three_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_three_tests_aov_1)
```
### 3 Tests Cluster PWC - Test 1
```{r, include=FALSE}
cluster_three_tests_aov_pwc_1 <- clusters_three_impact_test_complete %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_three_tests_aov_pwc_1 <- cluster_three_tests_aov_pwc_1 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_three_tests_aov_pwc_1 %>%
reactable(pagination = TRUE,
striped = TRUE,
outlined = TRUE,
searchable = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE)
```
### 3 Tests Cluster Summary - Test 2
```{r, include=TRUE}
cluster_smry_tbl(clusters_three_impact_test_complete, post_injury_2_score)
```
### 3 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster, post_injury_2_score))
```
### 3 Tests Cluster ANOVA - Test 2
```{r, include=FALSE}
cluster_three_tests_aov_2 <- aov(post_injury_2_score ~ symptom_cluster,
data = clusters_three_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_three_tests_aov_2)
```
### 3 Tests Cluster PWC - Test 2
```{r, include=FALSE}
cluster_three_tests_aov_pwc_2 <- clusters_three_impact_test_complete %>%
pairwise_t_test(
post_injury_2_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_three_tests_aov_pwc_2 <- cluster_three_tests_aov_pwc_2 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_three_tests_aov_pwc_2 %>%
reactable(pagination = TRUE,
striped = TRUE,
outlined = TRUE,
searchable = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE)
```
### 3 Tests Cluster Summary - Test 3
```{r, include=TRUE}
cluster_smry_tbl(clusters_three_impact_test_complete, post_injury_3_score)
```
### 3 Tests Cluster Boxplot - Test 3
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster, post_injury_3_score))
```
### 3 Tests Cluster ANOVA - Test 3
```{r, include=FALSE}
cluster_three_tests_aov_3 <- aov(post_injury_3_score ~ symptom_cluster,
data = clusters_three_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_three_tests_aov_3)
```
### 3 Tests Cluster PWC - Test 3
```{r, include=FALSE}
cluster_three_tests_aov_pwc_3 <- clusters_three_impact_test_complete %>%
pairwise_t_test(
post_injury_3_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_three_tests_aov_pwc_3 <- cluster_three_tests_aov_pwc_3 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_three_tests_aov_pwc_3 %>%
reactable(pagination = TRUE,
striped = TRUE,
outlined = TRUE,
searchable = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE)
```
Row {.tabset}
-----------------------------------------------------------------------
### 4 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_four_impact_test_1 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_1"))) %>%
mutate(#X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_1 <- clusters_four_impact_test_1 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_1_scaled <- map_df(clusters_four_impact_test_1, rescale01b)
clusters_four_impact_test_1_scaled <- bind_cols(clusters_four_impact_test_1,
clusters_four_impact_test_1_scaled)
clusters_four_impact_test_1_scaled <- clusters_four_impact_test_1_scaled %>%
select(-c(21:26))
clusters_four_impact_test_1_scaled[sapply(clusters_four_impact_test_1_scaled, is.character)] <- lapply(clusters_four_impact_test_1_scaled[sapply(clusters_four_impact_test_1_scaled, is.character)], as.factor)
str(clusters_four_impact_test_1_scaled)
clusters_four_impact_test_1_long <- clusters_four_impact_test_1_scaled %>%
pivot_longer(
cols = c(22:27),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
clusters_four_impact_test_2 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_2"))) %>%
mutate(#X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_2 <- clusters_four_impact_test_2 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_2_scaled <- map_df(clusters_four_impact_test_2, rescale01b)
clusters_four_impact_test_2_scaled <- bind_cols(clusters_four_impact_test_2,
clusters_four_impact_test_2_scaled)
clusters_four_impact_test_2_scaled <- clusters_four_impact_test_2_scaled %>%
select(-c(21:26))
clusters_four_impact_test_2_scaled[sapply(clusters_four_impact_test_2_scaled, is.character)] <- lapply(clusters_four_impact_test_2_scaled[sapply(clusters_four_impact_test_2_scaled, is.character)], as.factor)
str(clusters_four_impact_test_2_scaled)
clusters_four_impact_test_2_long <- clusters_four_impact_test_2_scaled %>%
pivot_longer(
cols = c(22:27),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
clusters_four_impact_test_3 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_3"))) %>%
mutate(#X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_3 <- clusters_four_impact_test_3 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_3_scaled <- map_df(clusters_four_impact_test_3, rescale01b)
clusters_four_impact_test_3_scaled <- bind_cols(clusters_four_impact_test_3,
clusters_four_impact_test_3_scaled)
clusters_four_impact_test_3_scaled <- clusters_four_impact_test_3_scaled %>%
select(-c(21:26))
clusters_four_impact_test_3_scaled[sapply(clusters_four_impact_test_3_scaled, is.character)] <- lapply(clusters_four_impact_test_3_scaled[sapply(clusters_four_impact_test_3_scaled, is.character)], as.factor)
str(clusters_four_impact_test_3_scaled)
clusters_four_impact_test_3_long <- clusters_four_impact_test_3_scaled %>%
pivot_longer(
cols = c(22:27),
names_to = "symptom_cluster",
values_to = "post_injury_3_score",
names_pattern = "(.*)_score_post_injury_3"
)
clusters_four_impact_test_4 <- four_impact_test %>%
select(c(1:20, contains("_post_injury_4"))) %>%
mutate(#X1 = as.character(X1),
row = as.character(row),
age_post_injury_1 = as.character(age_post_injury_1),
age_post_injury_2 = as.character(age_post_injury_2),
age_post_injury_3 = as.character(age_post_injury_3),
age_post_injury_4 = as.character(age_post_injury_4))
clusters_four_impact_test_4 <- clusters_four_impact_test_4 %>%
select(c(1:20, 44:49))
clusters_four_impact_test_4_scaled <- map_df(clusters_four_impact_test_4, rescale01b)
clusters_four_impact_test_4_scaled <- bind_cols(clusters_four_impact_test_4,
clusters_four_impact_test_4_scaled)
clusters_four_impact_test_4_scaled <- clusters_four_impact_test_4_scaled %>%
select(-c(21:26))
clusters_four_impact_test_4_scaled[sapply(clusters_four_impact_test_4_scaled, is.character)] <- lapply(clusters_four_impact_test_4_scaled[sapply(clusters_four_impact_test_4_scaled, is.character)], as.factor)
str(clusters_four_impact_test_4_scaled)
clusters_four_impact_test_4_long <- clusters_four_impact_test_4_scaled %>%
pivot_longer(
cols = c(22:27),
names_to = "symptom_cluster",
values_to = "post_injury_4_score",
names_pattern = "(.*)_score_post_injury_4"
)
clusters_four_impact_test_join_1 <- left_join(clusters_four_impact_test_1_long,
clusters_four_impact_test_2_long)
clusters_four_impact_test_join_2 <- left_join(clusters_four_impact_test_join_1,
clusters_four_impact_test_3_long)
clusters_four_impact_test_complete <- left_join(clusters_four_impact_test_join_2,
clusters_four_impact_test_4_long)
str(clusters_four_impact_test_complete)
```
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_1_score)
```
### 4 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_1_score))
```
### 4 Tests Cluster ANOVA - Test 1
```{r, include=FALSE}
cluster_four_tests_aov_1 <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_1)
```
### 4 Tests Cluster PWC - Test 1
```{r, include=FALSE}
cluster_four_tests_aov_pwc_1 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_1_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_1 <- cluster_four_tests_aov_pwc_1 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_1 %>%
reactable(pagination = TRUE,
striped = TRUE,
outlined = TRUE,
searchable = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE)
```
### 4 Tests Cluster Summary - Test 2
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_2_score)
```
### 4 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_2_score))
```
### 4 Tests Cluster ANOVA - Test 2
```{r, include=FALSE}
cluster_four_tests_aov_2 <- aov(post_injury_2_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_2)
```
### 4 Tests Cluster PWC - Test 2
```{r, include=FALSE}
cluster_four_tests_aov_pwc_2 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_2_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_2 <- cluster_four_tests_aov_pwc_2 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_2 %>%
reactable(pagination = TRUE,
striped = TRUE,
outlined = TRUE,
searchable = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE)
```
### 4 Tests Cluster Summary - Test 3
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_3_score)
```
### 4 Tests Cluster Boxplot - Test 3
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_3_score))
```
### 4 Tests Cluster ANOVA - Test 3
```{r, include=FALSE}
cluster_four_tests_aov_3 <- aov(post_injury_3_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_3)
```
### 4 Tests Cluster PWC - Test 3
```{r, include=FALSE}
cluster_four_tests_aov_pwc_3 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_3_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_3 <- cluster_four_tests_aov_pwc_3 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_3 %>%
reactable(pagination = TRUE,
striped = TRUE,
outlined = TRUE,
searchable = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE)
```
### 4 Tests Cluster Summary - Test 4
```{r, include=TRUE}
cluster_smry_tbl(clusters_four_impact_test_complete, post_injury_4_score)
```
### 4 Tests Cluster Boxplot - Test 4
```{r, include=TRUE}
ggplotly(cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster, post_injury_4_score))
```
### 4 Tests Cluster ANOVA - Test 4
```{r, include=FALSE}
cluster_four_tests_aov_4 <- aov(post_injury_4_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
```
```{r, include=TRUE}
summary(cluster_four_tests_aov_4)
```
### 4 Tests Cluster PWC - Test 4
```{r, include=FALSE}
cluster_four_tests_aov_pwc_4 <- clusters_four_impact_test_complete %>%
pairwise_t_test(
post_injury_4_score ~ symptom_cluster, paired = FALSE,
p.adjust.method = "bonferroni"
)
cluster_four_tests_aov_pwc_4 <- cluster_four_tests_aov_pwc_4 %>%
mutate_at(vars(p, p.adj), list(~ round(., 2)))
```
```{r, include=TRUE}
cluster_four_tests_aov_pwc_4 %>%
reactable(pagination = TRUE,
striped = TRUE,
outlined = TRUE,
searchable = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE)
```
# Gender-Cluster Interaction
Sidebar {.sidebar}
------------
**Research Questions:**
1. Does reported symptom severity differ by specific symptom cluster?
2. Do males and females report differing levels of symptom severity?
3. Are differences in symptom cluster severity ratings related to the differences between male and female symptom severity ratings?
**One Test Summary:**
* Significant interaction effect between symptom cluster and gender identified
* Non-significant PWC include:
+ Male sleep/male cognitive
+ Male ocular-motor/male anxiety-mood
+ Male headache migraine/female sleep
+ Male vestibular/female ocular-motor
+ Male headache-migraine/female cognitive
+ Female vestibular/female anxiety-mood
+ Female sleep/female cognitive
+ Sleep/cognitive
+ Ocular-motor/anxiety-mood
**Two Tests Summary:**
_Test One:_
* Significant interaction effect between symptom cluster and gender identified
* Non-significant PWC include:
+ Male sleep/male cognitive
+ Male ocular-motor/male anxiety-mood
+ Male sleep/female vestibular
+ Male cognitive/female vestibular
+ Male ocular-motor/female ocular-motor
+ Male vestibular/female ocular-motor
+ Male headache-migraine/female sleep
+ Male headache/female cognitive
+ Female sleep/female cognitive
+ Male sleep/female anxiety-mood
+ Female vestibular/female anxiety-mood
+ Sleep/cognitive
_Test Two:_
* Significant interaction effect between symptom cluster and gender identified
* Non-significant PWC include:
+ Male vestibular/male ocular-motor
+ Male vestibular/male anxiety-mood
+ Male sleep/male cognitive
+ Male ocular-motor/male anxiety-mood
+ Male vestibular/female vestibular
+ Male ocular-motor/female ocular-motor
+ Male vestibular/female ocular-motor
+ Male headache-migraine/female sleep
+ Male anxiety-mood/female ocular-motor
+ Female vestibular/female ocular-motor
+ Male headache-migraine/female cognitive
+ Female vestibular/female anxiety-mood
+ Male cognitive/female anxiety-mood
+ Male sleep/female anxiety-mood
+ Female sleep/female cognitive
+ Vestibular/anxiety-mood
+ Sleep/cognitive
**Three Tests Summary:**
_Test One:_
* Significant interaction effect between symptom cluster and gender identified
* Non-significant PWC include:
+ Male sleep/male cognitive
+ Male ocular-motor/male anxiety-mood
+ Male vestibular/female vestibular
+ Male sleep/female vestibular
+ Male ocular-motor/female ocular-motor
+ Male vestibular/female ocular-motor
+ Male cognitive/female sleep
+ Male headache-migraine/female sleep
+ Male anxiety-mood/female ocular-motor
+ Male headache-migraine/female cognitive
+ Female vestibular/female anxiety-mood
+ Male/cognitive/female anxiety-mood
+ Male sleep/female anxiety-mood
+ Male vestibular/female anxiety-mood
+ Female sleep/female cognitive
_Test Two:_
* Significant interaction effect between symptom cluster and gender identified
* Non-significant PWC include:
+ Male vestibular/male ocular-motor
+ Male vestibular/male anxiety-mood
+ Male sleep/male cognitive
+ Male ocular-motor/male anxiety-mood
+ Male vestibular/female vestibular
+ Male ocular-motor/female vestibular
+ Male anxiety-mood/female vestibular
+ Male ocular-motor/female ocular-motor
+ Male vestibular/female ocular-motor
+ Male cognitive/female sleep
+ Male headache-migraine/female sleep
+ Male sleep/female sleep
+ Male anxiety-mood/female ocular-motor
+ Female vestibular/female ocular-motor
+ Male cognitive/female cognitive
+ Male headache-migraine/female cognitive
+ Male sleep/female cognitive
+ Female sleep/female cognitive
+ Male vestibular/female anxiety-mood
+ Male ocular-motor/female anxiety-mood
+ Male cognitive/female anxiety-mood
+ Female vestibular/female anxiety-mood
+ Female ocular-motor/female anxiety-mood
+ Vestibular/ocular-motor
+ Ocular-motor/anxiety-mood
+ Vestibular/anxiety-mood
+ Sleep/cognitive
_Test Three:_
* Significant interaction effect between symptom cluster and gender identified
* Non-significant PWC include:
+ Male vestibular/male ocular-motor
+ Male vestibular/male anxiety-mood
+ Male headache-migraine/male cognitive
+ Male sleep/male cognitive
+ Male sleep/male headache-migraine
+ Male ocular-motor/male anxiety-mood
+ Male vestibular/female vestibular
+ Male sleep/female vestibular
+ Male ocular-motor/female vestibular
+ Male cognitive/female vestibular
+ Male ocular-motor/female ocular-motor
+ Male sleep/female ocular-motor
+ Male vestibular/female ocular-motor
+ Male cognitive/female sleep
+ Male headache-migraine/female sleep
+ Male sleep/female sleep
+ Male anxiety-mood/female ocular-motor
+ Female vestibular/female ocular-motor
+ Female sleep/female headache-migraine
+ Male sleep/female cognitive
+ Male headache-migraine/female cognitive
+ Male cognitive/female cognitive
+ Female sleep/female cognitive
+ Female headache-migraine/female cognitive
+ Male sleep/female anxiety-mood
+ Male headache-migraine/female anxiety-mood
+ Male cognitive/female anxiety-mood
+ Female vestibular/female anxiety-mood
+ Female sleep/female anxiety-mood
+ Female cognitive/female anxiety-mood
+ Vestibular/ocular-motor
+ Sleep/cognitive
+ Headache-migraine/cognitive
+ Vestibular/anxiety-mood
+ Ocular-motor/anxiety-mood
**Four Tests Summary:**
_Test One:_
* Interaction effect between symptom cluster and gender **not** significant
* Main effect of symptom cluster significant
* Main effect of gender significant
_Test Two:_
* Interaction effect between symptom cluster and gender **not** significant
* Main effect of symptom cluster significant
* Main effect of gender significant
_Test Three:_
* Interaction effect between symptom cluster and gender **not** significant
* Main effect of symptom cluster significant
* Main effect of gender significant
_Test Four:_
* Significant interaction effect between symptom cluster and gender identified
* Many non-significant PWC
* Significant PWC include:
+ Male vestibular/female sleep
+ Male anxiety-mood/female sleep
+ Male anxiety-mood/female headache-migraine
+ Male cognitive/female headache-migraine
+ Male headache-migraine/female headache-migraine
+ Male ocular-motor/female headache-migraine
+ Male sleep/female headache-migraine
+ Male vestibular/female headache-migraine
+ Female vestibular/female headache-migraine
+ Female ocular-motor/female headache-migraine
+ Male vestibular/female cognitive
+ Male sleep/female cognitive
+ Male ocular-motor/female cognitive
+ Male headache-migraine/female cognitive
+ Male cognitive/female cognitive
+ Male anxiety-mood/female cognitive
+ Female vestibular/Female cognitive
+ Female cognitive/female ocular-motor
+ Female headache-migraine/female anxiety-mood
+ Male/female
+ Vestibular/headache-migraine
+ Headache-migraine/anxiety-mood
+ Vestibular/cognitive
+ Ocular-motor/headache-migraine
Row {.tabset}
-----------------------------------------------------------------------
### 1 Test Cluster Summary
```{r, include=FALSE}
clusters_one_impact_test_scaled_long
scaled_cluster_gender_tbl <- function(df, a, b, c) {
df %>%
group_by({{a}}, {{b}}) %>%
summarize(Mean = mean({{c}}),
SD = sd({{c}}),
Min = min({{c}}),
Max = max({{c}})) %>%
mutate(Mean = as.numeric(Mean),
SD = as.numeric(SD),
Min = as.numeric(Min),
Max = as.numeric(Max)) %>%
arrange(desc(Mean)) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
gender = colDef(name = "Gender"),
symptom_cluster = colDef(name = "Symptom Cluster"),
Mean = colDef(format = colFormat(separators = TRUE)),
SD = colDef(format = colFormat(separators = TRUE)),
Min = colDef(format = colFormat(separators = TRUE)),
Max = colDef(format = colFormat(separators = TRUE))
),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
}
scaled_cluster_gender_tbl(clusters_one_impact_test_scaled_long,
gender,
symptom_cluster,
post_injury_1_score)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_one_impact_test_scaled_long,
gender,
symptom_cluster,
post_injury_1_score)
```
### Non-Normalized Summary
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_one_impact_test_long,
gender,
symptom_cluster,
post_injury_1_score)
```
### 1 Test Cluster Boxplot
```{r, include=FALSE}
ggplot(clusters_one_impact_test_scaled_long,
aes(symptom_cluster, post_injury_1_score, color = gender)) +
geom_boxplot() +
scale_color_OkabeIto() +
coord_flip() +
labs(x = "",
y = "Scaled Severity Score") +
theme(legend.title = element_blank())
gender_cluster_bxp <- function(df, a, b, c) {
ggplot(df,
aes({{a}}, {{b}}, color = {{c}})) +
geom_boxplot() +
scale_color_OkabeIto() +
coord_flip() +
labs(x = "",
y = "Scaled Severity Score") +
theme(legend.title = element_blank())
}
gender_cluster_bxp(clusters_one_impact_test_scaled_long,
symptom_cluster,
post_injury_1_score,
gender)
```
```{r, include=TRUE}
ggplotly(gender_cluster_bxp(clusters_one_impact_test_scaled_long,
symptom_cluster,
post_injury_1_score,
gender))
```
### 1 Test Cluster Two-Way ANOVA
```{r, include=FALSE}
cluster_one_test_aov2 <- aov(post_injury_1_score ~ symptom_cluster * gender,
data = clusters_one_impact_test_scaled_long)
library(car)
summary(cluster_one_test_aov2)
cluster_one_test_aov2
Anova(cluster_one_test_aov2, type = "III")
TukeyHSD(cluster_one_test_aov2)
TukeyHSD(cluster_one_test_aov2, which = "gender")
TukeyHSD(cluster_one_test_aov2, which = "symptom_cluster")
library(broom)
res_1 <- TukeyHSD(cluster_one_test_aov2)
tidy(res_1) %>%
reactable()
a_1 <- Anova(cluster_one_test_aov2, type = "III")
summary(a_1)
reactable(a_1)
anova_react <- function(df) {
df %>%
reactable(
defaultColDef = colDef(align = "center",
format = colFormat(digits = 2, separators = TRUE)),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
}
pwc_react <- function(df) {
df %>%
reactable(
defaultColDef = colDef(align = "center",
format = colFormat(digits = 3, separators = TRUE)),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE,
searchable = TRUE
)
}
```
```{r, include=TRUE}
anova_react(a_1)
```
### 1 Test Cluster PWC
```{r, include=TRUE}
tidy(res_1) %>%
pwc_react()
```
Row {.tabset}
-----------------------------------------------------------------------
### 2 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_two_impact_test_complete[sapply(clusters_two_impact_test_complete, is.character)] <- lapply(clusters_two_impact_test_complete[sapply(clusters_two_impact_test_complete, is.character)], as.factor)
str(clusters_two_impact_test_complete)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_two_impact_test_complete,
gender,
symptom_cluster,
post_injury_1_score)
```
### Non-Normalized Summary
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_two_impact_test_1_long,
gender,
symptom_cluster,
post_injury_1_score)
```
### 2 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(gender_cluster_bxp(clusters_two_impact_test_complete,
symptom_cluster,
post_injury_1_score,
gender))
```
### 2 Tests Cluster Two-Way ANOVA - Test 1
```{r, include=FALSE}
cluster_two_tests_aov2_1 <- aov(post_injury_1_score ~ symptom_cluster * gender,
data = clusters_two_impact_test_complete)
summary(cluster_two_tests_aov2_1)
cluster_two_tests_aov2_1
Anova(cluster_two_tests_aov2_1, type = "III")
TukeyHSD(cluster_two_tests_aov2_1)
res_2_1 <- TukeyHSD(cluster_two_tests_aov2_1)
a_2_1 <- Anova(cluster_two_tests_aov2_1, type = "III")
```
```{r, include=TRUE}
anova_react(a_2_1)
```
### 2 Tests Cluster PWC - Test 1
```{r, include=TRUE}
tidy(res_2_1) %>%
pwc_react()
```
### 2 Tests Cluster Summary - Test 2
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_two_impact_test_complete,
gender,
symptom_cluster,
post_injury_2_score)
```
### 2 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(gender_cluster_bxp(clusters_two_impact_test_complete,
symptom_cluster,
post_injury_2_score,
gender))
```
### 2 Tests Cluster Two-Way ANOVA - Test 2
```{r, include=FALSE}
cluster_two_tests_aov2_2 <- aov(post_injury_2_score ~ symptom_cluster * gender,
data = clusters_two_impact_test_complete)
summary(cluster_two_tests_aov2_2)
cluster_two_tests_aov2_2
Anova(cluster_two_tests_aov2_2, type = "III")
TukeyHSD(cluster_two_tests_aov2_2)
res_2_2 <- TukeyHSD(cluster_two_tests_aov2_2)
a_2_2 <- Anova(cluster_two_tests_aov2_2, type = "III")
```
```{r, include=TRUE}
anova_react(a_2_2)
```
### 2 Tests Cluster PWC - Test 1
```{r, include=TRUE}
tidy(res_2_2) %>%
pwc_react()
```
Row {.tabset}
-----------------------------------------------------------------------
### 3 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_three_impact_test_complete[sapply(clusters_three_impact_test_complete, is.character)] <- lapply(clusters_three_impact_test_complete[sapply(clusters_three_impact_test_complete, is.character)], as.factor)
str(clusters_three_impact_test_complete)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_three_impact_test_complete,
gender,
symptom_cluster,
post_injury_1_score)
```
### 3 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(gender_cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster,
post_injury_1_score,
gender))
```
### 3 Tests Cluster Two-Way ANOVA - Test 1
```{r, include=FALSE}
cluster_three_tests_aov2_1 <- aov(post_injury_1_score ~ symptom_cluster * gender,
data = clusters_three_impact_test_complete)
summary(cluster_three_tests_aov2_1)
cluster_three_tests_aov2_1
Anova(cluster_three_tests_aov2_1, type = "III")
TukeyHSD(cluster_three_tests_aov2_1)
res_3_1 <- TukeyHSD(cluster_three_tests_aov2_1)
a_3_1 <- Anova(cluster_three_tests_aov2_1, type = "III")
```
```{r, include=TRUE}
anova_react(a_3_1)
```
### 3 Tests Cluster PWC - Test 1
```{r, include=TRUE}
tidy(res_3_1) %>%
pwc_react()
```
### 3 Tests Cluster Summary - Test 2
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_three_impact_test_complete,
gender,
symptom_cluster,
post_injury_2_score)
```
### 3 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(gender_cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster,
post_injury_2_score,
gender))
```
### 3 Tests Cluster Two-Way ANOVA - Test 2
```{r, include=FALSE}
cluster_three_tests_aov2_2 <- aov(post_injury_2_score ~ symptom_cluster * gender,
data = clusters_three_impact_test_complete)
summary(cluster_three_tests_aov2_2)
cluster_three_tests_aov2_2
Anova(cluster_three_tests_aov2_2, type = "III")
TukeyHSD(cluster_three_tests_aov2_2)
res_3_2 <- TukeyHSD(cluster_three_tests_aov2_2)
a_3_2 <- Anova(cluster_three_tests_aov2_2, type = "III")
```
```{r, include=TRUE}
anova_react(a_3_2)
```
### 3 Tests Cluster PWC - Test 2
```{r, include=TRUE}
tidy(res_3_2) %>%
pwc_react()
```
### 3 Tests Cluster Summary - Test 3
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_three_impact_test_complete,
gender,
symptom_cluster,
post_injury_3_score)
```
### 3 Tests Cluster Boxplot - Test 3
```{r, include=TRUE}
ggplotly(gender_cluster_bxp(clusters_three_impact_test_complete,
symptom_cluster,
post_injury_3_score,
gender))
```
### 3 Tests Cluster Two-Way ANOVA - Test 3
```{r, include=FALSE}
cluster_three_tests_aov2_3 <- aov(post_injury_3_score ~ symptom_cluster * gender,
data = clusters_three_impact_test_complete)
summary(cluster_three_tests_aov2_3)
cluster_three_tests_aov2_3
Anova(cluster_three_tests_aov2_3, type = "III")
TukeyHSD(cluster_three_tests_aov2_3)
res_3_3 <- TukeyHSD(cluster_three_tests_aov2_3)
a_3_3 <- Anova(cluster_three_tests_aov2_3, type = "III")
```
```{r, include=TRUE}
anova_react(a_3_3)
```
### 3 Tests Cluster PWC - Test 3
```{r, include=TRUE}
tidy(res_3_3) %>%
pwc_react()
```
Row {.tabset}
-----------------------------------------------------------------------
### 4 Tests Cluster Summary - Test 1
```{r, include=FALSE}
clusters_four_impact_test_complete[sapply(clusters_four_impact_test_complete, is.character)] <- lapply(clusters_four_impact_test_complete[sapply(clusters_four_impact_test_complete, is.character)], as.factor)
str(clusters_four_impact_test_complete)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_four_impact_test_complete,
gender,
symptom_cluster,
post_injury_1_score)
```
### 4 Tests Cluster Boxplot - Test 1
```{r, include=TRUE}
ggplotly(gender_cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster,
post_injury_1_score,
gender))
```
### 4 Tests Cluster Two-Way ANOVA - Test 1
```{r, include=FALSE}
cluster_four_tests_aov2_1 <- aov(post_injury_1_score ~ symptom_cluster * gender,
data = clusters_four_impact_test_complete)
summary(cluster_four_tests_aov2_1)
cluster_four_tests_aov2_1
Anova(cluster_four_tests_aov2_1, type = "III")
TukeyHSD(cluster_four_tests_aov2_1)
res_4_1 <- TukeyHSD(cluster_four_tests_aov2_1)
a_4_1 <- Anova(cluster_four_tests_aov2_1, type = "III")
four_tests_main_effect_cluster_1 <- aov(post_injury_1_score ~ symptom_cluster,
data = clusters_four_impact_test_complete)
summary(four_tests_main_effect_cluster_1)
four_tests_main_effect_gender_1 <- aov(post_injury_1_score ~ gender,
data = clusters_four_impact_test_complete)
summary(four_tests_main_effect_gender_1)
cluster_main_effect_1 <- Anova(four_tests_main_effect_cluster_1)
summary(cluster_main_effect_1)
gender_main_effect_1 <- Anova(four_tests_main_effect_gender_1, type = "III")
summary(gender_main_effect_1)
cluster_4_1_pwc <- TukeyHSD(four_tests_main_effect_cluster_1)
gender_4_1_pwc <- TukeyHSD(four_tests_main_effect_gender_1)
```
```{r, include=TRUE}
anova_react(a_4_1)
```
### 4 Tests Cluster Interaction PWC - Test 1
```{r, include=TRUE}
tidy(res_4_1) %>%
pwc_react()
```
### 4 Tests - Test 1 One-Way ANOVA Main Effect Cluster
```{r, include=TRUE}
anova_react(cluster_main_effect_1)
```
### 4 Tests - Test 1 One-Way ANOVA Main Effect Cluster PWC
```{r, include=TRUE}
tidy(cluster_4_1_pwc) %>%
pwc_react()
```
### 4 Tests - Test 1 One-Way ANOVA Main Effect Gender
```{r, include=TRUE}
anova_react(gender_main_effect_1)
```
### 4 Tests - Test 1 One-Way ANOVA Main Effect Gender PWC
```{r, include=TRUE}
tidy(gender_4_1_pwc) %>%
pwc_react()
```
### 4 Tests Cluster Summary - Test 2
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_four_impact_test_complete,
gender,
symptom_cluster,
post_injury_2_score)
```
### 4 Tests Cluster Boxplot - Test 2
```{r, include=TRUE}
ggplotly(gender_cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster,
post_injury_2_score,
gender))
```
### 4 Tests Cluster Two-Way ANOVA - Test 2
```{r, include=FALSE}
cluster_four_tests_aov2_2 <- aov(post_injury_2_score ~ symptom_cluster * gender,
data = clusters_four_impact_test_complete)
summary(cluster_four_tests_aov2_2)
cluster_four_tests_aov2_2
Anova(cluster_four_tests_aov2_2, type = "III")
TukeyHSD(cluster_four_tests_aov2_2)
res_4_2 <- TukeyHSD(cluster_four_tests_aov2_2)
a_4_2 <- Anova(cluster_four_tests_aov2_2, type = "III")
```
```{r, include=TRUE}
anova_react(a_4_2)
```
### 4 Tests Cluster PWC - Test 2
```{r, include=TRUE}
tidy(res_4_2) %>%
pwc_react()
```
### 4 Tests Cluster Summary - Test 3
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_four_impact_test_complete,
gender,
symptom_cluster,
post_injury_3_score)
```
### 4 Tests Cluster Boxplot - Test 3
```{r, include=TRUE}
ggplotly(gender_cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster,
post_injury_3_score,
gender))
```
### 4 Tests Cluster Two-Way ANOVA - Test 3
```{r, include=FALSE}
cluster_four_tests_aov2_3 <- aov(post_injury_3_score ~ symptom_cluster * gender,
data = clusters_four_impact_test_complete)
summary(cluster_four_tests_aov2_3)
cluster_four_tests_aov2_3
Anova(cluster_four_tests_aov2_3, type = "III")
TukeyHSD(cluster_four_tests_aov2_3)
res_4_3 <- TukeyHSD(cluster_four_tests_aov2_3)
a_4_3 <- Anova(cluster_four_tests_aov2_3, type = "III")
```
```{r, include=TRUE}
anova_react(a_4_3)
```
### 4 Tests Cluster PWC - Test 3
```{r, include=TRUE}
tidy(res_4_3) %>%
pwc_react()
```
### 4 Tests Cluster Summary - Test 4
```{r, include=TRUE}
scaled_cluster_gender_tbl(clusters_four_impact_test_complete,
gender,
symptom_cluster,
post_injury_4_score)
```
### 4 Tests Cluster Boxplot - Test 4
```{r, include=TRUE}
ggplotly(gender_cluster_bxp(clusters_four_impact_test_complete,
symptom_cluster,
post_injury_4_score,
gender))
```
### 4 Tests Cluster Two-Way ANOVA - Test 4
```{r, include=FALSE}
cluster_four_tests_aov2_4 <- aov(post_injury_4_score ~ symptom_cluster * gender,
data = clusters_four_impact_test_complete)
summary(cluster_four_tests_aov2_4)
cluster_four_tests_aov2_4
Anova(cluster_four_tests_aov2_4, type = "III")
TukeyHSD(cluster_four_tests_aov2_4)
res_4_4 <- TukeyHSD(cluster_four_tests_aov2_4)
a_4_4 <- Anova(cluster_four_tests_aov2_4, type = "III")
```
```{r, include=TRUE}
anova_react(a_4_4)
```
### 4 Tests Cluster PWC - Test 4
```{r, include=TRUE}
tidy(res_4_4) %>%
pwc_react()
```
# Test 1 Symptom Comparison
Sidebar {.sidebar}
------------
* Two-Way ANOVA between the number of tests completed and gender did not find a significant interaction effect for the overall, post-injury 1 total symptom severity score, but the main effects of number of tests completed and gender were both significant
* Follow up One-Way ANOVAs were completed on the main effects and were both significant
* Females reported higher total symptom severity ratings than males at time of post-injury 1
* All PWC between the number of tests completed were significant, indicating that individuals who end up taking more tests are more symptomatic than individuals who complete less tests. This should provide more support that symptom severity at time of first testing may correlate or predict recovery duration
Row {.tabset}
-----------------------------------------------------------------------
### Total Symptom Score Summary
```{r, include=FALSE}
names(one_impact_test)
names(two_impact_test)
names(three_impact_test)
names(four_impact_test)
test_one_comparison <- function(df) {
df %>%
select(passport_id,
test_date_post_injury_1,
gender,
current_sport_post_injury_1,
total_symptom_score_post_injury_1,
headache_migraine_cluster_score_post_injury_1,
cognitive_cluster_score_post_injury_1,
anxiety_mood_cluster_score_post_injury_1,
ocular_motor_cluster_score_post_injury_1,
vestibular_cluster_score_post_injury_1,
sleep_cluster_score_post_injury_1)
}
one_impact_comparison <- test_one_comparison(one_impact_test)
two_impact_comparison <- test_one_comparison(two_impact_test)
three_impact_comparison <- test_one_comparison(three_impact_test)
four_impact_comparison <- test_one_comparison(four_impact_test)
all_test_one_comparison <- bind_rows(one_impact_comparison,
two_impact_comparison,
three_impact_comparison,
four_impact_comparison,
.id = "total_tests_completed")
str(all_test_one_comparison)
all_test_one_comparison <- all_test_one_comparison %>%
mutate(total_tests_completed = as.factor(total_tests_completed),
gender = as.factor(gender))
str(all_test_one_comparison)
all_test_one_comparison_scaled <- map_df(all_test_one_comparison, rescale01b)
col_data <- all_test_one_comparison %>%
select(1:5)
all_test_one_comparison_scaled <- bind_cols(col_data, all_test_one_comparison_scaled)
str(all_test_one_comparison_scaled)
```
```{r, include=FALSE}
cluster_smry_tbl2 <- function(df, x) {
df %>%
group_by(total_tests_completed, gender) %>%
summarize(mean = mean({{x}}),
sd = sd({{x}}),
min = min({{x}}),
max = max({{x}}),
total = length({{x}})) %>%
mutate(mean = as.numeric(mean),
sd = as.numeric(sd),
min = as.numeric(min),
max = as.numeric(max),
total = as.numeric(total)) %>%
reactable(
columns = list(
total_tests_completed = colDef(name = "Total Tests Completed",
align = "center"),
gender = colDef(name = "Gender",
align = "center"),
mean = colDef(name = "Mean",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
sd = colDef(name = "SD",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
min = colDef(name = "Min",
align = "center",
format = colFormat(separators = TRUE)),
max = colDef(name = "Max",
align = "center",
format = colFormat(separators = TRUE)),
total = colDef(name = "Total",
align = "center",
format = colFormat(separators = TRUE, suffix = " concussions"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
}
cluster_smry_tbl3 <- function(df, x) {
df %>%
group_by(total_tests_completed) %>%
summarize(mean = mean({{x}}),
sd = sd({{x}}),
min = min({{x}}),
max = max({{x}}),
total = length({{x}})) %>%
mutate(mean = as.numeric(mean),
sd = as.numeric(sd),
min = as.numeric(min),
max = as.numeric(max),
total = as.numeric(total)) %>%
reactable(
columns = list(
total_tests_completed = colDef(name = "Total Tests Completed",
align = "center"),
mean = colDef(name = "Mean",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
sd = colDef(name = "SD",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
min = colDef(name = "Min",
align = "center",
format = colFormat(separators = TRUE)),
max = colDef(name = "Max",
align = "center",
format = colFormat(separators = TRUE)),
total = colDef(name = "Total",
align = "center",
format = colFormat(separators = TRUE, suffix = " concussions"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
}
cluster_smry_tbl4 <- function(df, x) {
df %>%
group_by(gender) %>%
summarize(mean = mean({{x}}),
sd = sd({{x}}),
min = min({{x}}),
max = max({{x}}),
total = length({{x}})) %>%
mutate(mean = as.numeric(mean),
sd = as.numeric(sd),
min = as.numeric(min),
max = as.numeric(max),
total = as.numeric(total)) %>%
reactable(
columns = list(
gender = colDef(name = "Gender",
align = "center"),
mean = colDef(name = "Mean",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
sd = colDef(name = "SD",
align = "center",
format = colFormat(digits = 2, separators = TRUE)),
min = colDef(name = "Min",
align = "center",
format = colFormat(separators = TRUE)),
max = colDef(name = "Max",
align = "center",
format = colFormat(separators = TRUE)),
total = colDef(name = "Total",
align = "center",
format = colFormat(separators = TRUE, suffix = " concussions"))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
}
```
```{r, include=TRUE}
cluster_smry_tbl2(all_test_one_comparison, total_symptom_score_post_injury_1)
```
### Total Symptom Score Summary - Gender
```{r, include=TRUE}
cluster_smry_tbl4(all_test_one_comparison, total_symptom_score_post_injury_1)
```
### Total Symptom Score Summary - Number of Tests Completed
```{r, include=TRUE}
cluster_smry_tbl3(all_test_one_comparison, total_symptom_score_post_injury_1)
```
### Total Symptom Score Boxplot
```{r, include=FALSE}
test_1_bxp <- function(df, a, b, c) {
ggplot(df,
aes({{a}}, {{b}}, color = {{c}})) +
geom_boxplot() +
scale_color_OkabeIto() +
labs(x = "Number of Tests Completed",
y = "Test 1 Severity Score") +
theme(legend.title = element_blank())
}
```
```{r, include=TRUE}
ggplotly(test_1_bxp(all_test_one_comparison,
total_tests_completed,
total_symptom_score_post_injury_1,
gender))
```
Row {.tabset}
-----------------------------------------------------------------------
### Total Symptom Score Two-Way ANOVA
```{r, include=FALSE}
total_symptom_score_aov <- aov(total_symptom_score_post_injury_1 ~ total_tests_completed * gender, data = all_test_one_comparison)
summary(total_symptom_score_aov)
total_symptom_score_aov_gender <- aov(total_symptom_score_post_injury_1 ~ gender,
data = all_test_one_comparison)
summary(total_symptom_score_aov_gender)
total_symptom_score_aov_tests <- aov(total_symptom_score_post_injury_1 ~ total_tests_completed, data = all_test_one_comparison)
summary(total_symptom_score_aov_tests)
Anova(total_symptom_score_aov, type = "III")
Anova(total_symptom_score_aov_gender, type = "III")
Anova(total_symptom_score_aov_tests, type = "III")
TukeyHSD(total_symptom_score_aov)
TukeyHSD(total_symptom_score_aov_gender)
TukeyHSD(total_symptom_score_aov_tests)
total_symptom_interaction_results <- Anova(total_symptom_score_aov, type = "III")
total_symptom_gender_results <- Anova(total_symptom_score_aov_gender, type = "III")
total_symptom_tests_results <- Anova(total_symptom_score_aov_tests, type = "III")
total_pwc <- TukeyHSD(total_symptom_score_aov)
gender_pwc <- TukeyHSD(total_symptom_score_aov_gender)
tests_pwc <- TukeyHSD(total_symptom_score_aov_tests)
```
```{r, include=TRUE}
anova_react(total_symptom_interaction_results)
```
### Total Symptom Score Two-Way ANOVA Interaction PWC
```{r, include=TRUE}
tidy(total_pwc) %>%
pwc_react()
```
### Total Symptom Score One-Way ANOVA Main Effect Gender
```{r, include=TRUE}
anova_react(total_symptom_gender_results)
```
### Total Symptom Score One-Way ANOVA Main Effect Gender PWC
```{r, include=TRUE}
tidy(gender_pwc) %>%
pwc_react()
```
### Total Symptom Score One-Way ANOVA Main Effect Number of Tests Completed
```{r, include=TRUE}
anova_react(total_symptom_tests_results)
```
### Total Symptom Score One-Way ANOVA Main Effect Number of Tests Completed PWC
```{r, include=TRUE}
tidy(tests_pwc) %>%
pwc_react()
```
# Time
Sidebar {.sidebar}
------------
This tab provides summary tables and plots for the duration of time between completion of ImPACT tests for the samples that completed two, three, or four tests.
The number of days between tests was influential in the identification of data analyzed throughout this project. Following the results of Tamura et al. (2020) that identified the majority of students complete the RTP protocol within 30 days, the following filter restrictions were placed:
_Two Tests:_
* Students who completed tests 1 and 2 within 30 days
_Three Tests:_
* Students who completed tests 1 and 2 within 30 days
* Students who completed tests 2 and 3 within 30 days
* Students who completed tests 1 and 3 within 60 days
_Four Tests:_
* Students who completed tests 1 and 2 within 30 days
* Students who completed tests 2 and 3 within 30 days
* Students who completed tests 3 and 4 within 30 days
* Students who completed tests 1 and 4 within 90 days
Row {.tabset}
-----------------------------------------------------------------------
### Two Tests - Time Between Tests 1/2
```{r, include=FALSE}
names(two_impact_test)
names(three_impact_test)
names(four_impact_test)
time_react <- function(df, var) {
df %>%
summarize(Mean = mean({{var}}),
SD = sd({{var}}),
Min = min({{var}}),
Max = max({{var}}),
Total = length({{var}})) %>%
mutate_if(is.numeric, round, 2) %>%
reactable(columns = list(
Mean = colDef(format = colFormat(separators = TRUE, suffix = " days")),
SD = colDef(format = colFormat(separators = TRUE, suffix = " days")),
Min = colDef(format = colFormat(separators = TRUE, suffix = " days")),
Max = colDef(format = colFormat(separators = TRUE, suffix = " days")),
Total = colDef(format = colFormat(separators = TRUE, suffix = " individuals"))
))
}
time_hist <- function(df, x) {
ggplot(df, aes({{x}})) +
geom_histogram(fill = "#56B4E9",
color = "white",
alpha = 0.9,
bins = 20) +
labs(x = "Number of Days",
y = "Number of Individuals")
}
```
```{r, include=TRUE}
time_react(two_impact_test, dys_btwn_tst_dts)
```
### Two Tests - Time Between Tests 1/2
```{r, include=TRUE}
ggplotly(time_hist(two_impact_test, dys_btwn_tst_dts))
```
Row {.tabset}
-----------------------------------------------------------------------
### Three Tests - Time Between Tests 1/2
```{r, include=TRUE}
time_react(three_impact_test, dys_btwn_tst_1_2)
```
### Three Tests - Time Between Tests 1/2
```{r, include=TRUE}
ggplotly(time_hist(three_impact_test, dys_btwn_tst_1_2))
```
### Three Tests - Time Between Tests 1/3
```{r, include=TRUE}
time_react(three_impact_test, dys_btwn_tst_1_3)
```
### Three Tests - Time Between Tests 1/3
```{r, include=TRUE}
ggplotly(time_hist(three_impact_test, dys_btwn_tst_1_3))
```
### Three Tests - Time Between Tests 2/3
```{r, include=TRUE}
time_react(three_impact_test, dys_btwn_tst_2_3)
```
### Three Tests - Time Between Tests 2/3
```{r, include=TRUE}
ggplotly(time_hist(three_impact_test, dys_btwn_tst_2_3))
```
Row {.tabset}
-----------------------------------------------------------------------
### Four Tests - Time Between Tests 1/2
```{r, include=TRUE}
time_react(four_impact_test, dys_btwn_tst_1_2)
```
### Four Tests - Time Between Tests 1/2
```{r, include=TRUE}
ggplotly(time_hist(four_impact_test, dys_btwn_tst_1_2))
```
### Four Tests - Time Between Tests 2/3
```{r, include=TRUE}
time_react(four_impact_test, dys_btwn_tst_2_3)
```
### Four Tests - Time Between Tests 2/3
```{r, include=TRUE}
ggplotly(time_hist(four_impact_test, dys_btwn_tst_2_3))
```
### Four Tests - Time Between Tests 3/4
```{r, include=TRUE}
time_react(four_impact_test, dys_btwn_tst_3_4)
```
### Four Tests - Time Between Tests 3/4
```{r, include=TRUE}
ggplotly(time_hist(four_impact_test, dys_btwn_tst_3_4))
```
### Four Tests - Time Between Tests 1/4
```{r, include=TRUE}
time_react(four_impact_test, dys_btwn_tst_1_4)
```
### Four Tests - Time Between Tests 1/4
```{r, include=TRUE}
ggplotly(time_hist(four_impact_test, dys_btwn_tst_1_4))
```
```{r, include=FALSE}
# create_react_time <- function(df, var) {
# df %>%
# summarize(Mean = mean({{var}}),
# SD = sd({{var}}),
# Min = min({{var}}),
# Max = max({{var}}),
# Total = length({{var}})) %>%
# mutate_if(is.numeric, round, 2) %>%
# reactable(columns = list(
# Mean = colDef(format = colFormat(separators = TRUE, suffix = " days")),
# SD = colDef(format = colFormat(separators = TRUE, suffix = " days")),
# Min = colDef(format = colFormat(separators = TRUE, suffix = " days")),
# Max = colDef(format = colFormat(separators = TRUE, suffix = " days")),
# Total = colDef(format = colFormat(separators = TRUE, suffix = " concussions"))
# ))
# }
#
# #join baseline dates with impact post injury tests
#
# str(baseline_dates)
# str(one_impact_test)
#
# baseline_dates %>%
# count(passport_id)
#
# bl_one_impact_test <- right_join(baseline_dates, one_impact_test)
#
# bl_one_impact_test_2 <- left_join(baseline_dates, one_impact_test)
#
# bl_one_impact_test_3 <- bind_cols(baseline_dates, one_impact_test)
```
# Rate
Sidebar {.sidebar}
------------
Row {.tabset}
-----------------------------------------------------------------------
### Two Tests Percent Decrease
```{r, include=FALSE}
head(clusters_two_impact_test_1)
two_tests_cluster_scores_join <- left_join(clusters_two_impact_test_1,
clusters_two_impact_test_2)
two_tests_cluster_scores_join <- two_tests_cluster_scores_join %>%
mutate(total_symptom_score_post_injury_1 = headache_migraine_cluster_score_post_injury_1 +
cognitive_cluster_score_post_injury_1 +
anxiety_mood_cluster_score_post_injury_1 +
ocular_motor_cluster_score_post_injury_1 +
vestibular_cluster_score_post_injury_1 +
sleep_cluster_score_post_injury_1,
total_symptom_score_post_injury_2 = headache_migraine_cluster_score_post_injury_2 +
cognitive_cluster_score_post_injury_2 +
anxiety_mood_cluster_score_post_injury_2 +
ocular_motor_cluster_score_post_injury_2 +
vestibular_cluster_score_post_injury_2 +
sleep_cluster_score_post_injury_2)
two_tests_cluster_scores_means <- two_tests_cluster_scores_join %>%
select(c(13:26)) %>%
summarize(mean_headache_score_1 = mean(headache_migraine_cluster_score_post_injury_1),
mean_headache_score_2 = mean(headache_migraine_cluster_score_post_injury_2),
mean_cognitive_score_1 = mean(cognitive_cluster_score_post_injury_1),
mean_cognitive_score_2 = mean(cognitive_cluster_score_post_injury_2),
mean_anxiety_score_1 = mean(anxiety_mood_cluster_score_post_injury_1),
mean_anxiety_score_2 = mean(anxiety_mood_cluster_score_post_injury_2),
mean_ocular_motor_score_1 = mean(ocular_motor_cluster_score_post_injury_1),
mean_ocular_motor_score_2 = mean(ocular_motor_cluster_score_post_injury_2),
mean_vestibular_score_1 = mean(vestibular_cluster_score_post_injury_1),
mean_vestibular_score_2 = mean(vestibular_cluster_score_post_injury_2),
mean_sleep_score_1 = mean(sleep_cluster_score_post_injury_1),
mean_sleep_score_2 = mean(sleep_cluster_score_post_injury_2),
mean_total_score_1 = mean(total_symptom_score_post_injury_1),
mean_total_score_2 = mean(total_symptom_score_post_injury_2)) %>%
mutate_if(is.numeric, ~round(., 2))
two_tests_cluster_scores_percent_decrease <- two_tests_cluster_scores_means %>%
mutate(perc_decrease_headache_score = (mean_headache_score_1 - mean_headache_score_2) / mean_headache_score_1 * 100,
perc_decrease_cognitive_score = (mean_cognitive_score_1 - mean_cognitive_score_2) / mean_cognitive_score_1 * 100,
perc_decrease_anxiety_score = (mean_anxiety_score_1 - mean_anxiety_score_2) / mean_anxiety_score_1 * 100,
perc_decrease_ocular_motor_score = (mean_ocular_motor_score_1 - mean_ocular_motor_score_2) / mean_ocular_motor_score_1 * 100,
perc_decrease_vestibular_score = (mean_vestibular_score_1 - mean_vestibular_score_2) / mean_vestibular_score_1 * 100,
perc_decrease_sleep_score = (mean_sleep_score_1 - mean_sleep_score_2) / mean_sleep_score_1 * 100,
perc_decrease_total_score = (mean_total_score_1 - mean_total_score_2) / mean_total_score_1 * 100) %>%
mutate_if(is.numeric, ~round(., 2))
two_tests_cluster <- c("Headache-Migraine",
"Cognitive",
"Anxiety-Mood",
"Ocular-Motor",
"Vestibular",
"Sleep",
"Total Symptom Score")
two_tests_test_one_mean <- c(3.02, 2.98, 1.91, 0.36, 2.01, 3.02, 13.31)
two_tests_test_two_mean <- c(0.94, 0.95, 0.56, 0.10, 0.45, 0.94, 3.94)
two_tests_percent_decrease <- c(.6887, .6812, .7068, .7222, .7761, .6887, .7040)
two_tests_percent_decrease_clean <- data_frame(two_tests_cluster,
two_tests_test_one_mean,
two_tests_test_two_mean,
two_tests_percent_decrease)
str(two_tests_percent_decrease_clean)
two_tests_percent_decrease_tbl <- two_tests_percent_decrease_clean %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
two_tests_cluster = colDef(name = "Symptom Cluster"),
two_tests_test_one_mean = colDef(name = "Test One Mean"),
two_tests_test_two_mean = colDef(name = "Test Two Mean"),
two_tests_percent_decrease = colDef(name = "Percent Decrease",
format = colFormat(percent = TRUE))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
```
```{r, include=TRUE}
two_tests_percent_decrease_tbl
```
Row {.tabset}
-----------------------------------------------------------------------
### Three Tests Percent Decrease
```{r, include=FALSE}
head(clusters_three_impact_test_1)
head(clusters_three_impact_test_2)
head(clusters_three_impact_test_3)
three_tests_cluster_scores_join_1 <- left_join(clusters_three_impact_test_1,
clusters_three_impact_test_2)
three_tests_cluster_scores_join_all <- left_join(three_tests_cluster_scores_join_1,
clusters_three_impact_test_3)
three_tests_cluster_scores_join_all <- three_tests_cluster_scores_join_all %>%
mutate(total_symptom_score_post_injury_1 = headache_migraine_cluster_score_post_injury_1 +
cognitive_cluster_score_post_injury_1 +
anxiety_mood_cluster_score_post_injury_1 +
ocular_motor_cluster_score_post_injury_1 +
vestibular_cluster_score_post_injury_1 +
sleep_cluster_score_post_injury_1,
total_symptom_score_post_injury_2 = headache_migraine_cluster_score_post_injury_2 +
cognitive_cluster_score_post_injury_2 +
anxiety_mood_cluster_score_post_injury_2 +
ocular_motor_cluster_score_post_injury_2 +
vestibular_cluster_score_post_injury_2 +
sleep_cluster_score_post_injury_2,
total_symptom_score_post_injury_3 = headache_migraine_cluster_score_post_injury_3 +
cognitive_cluster_score_post_injury_3 +
anxiety_mood_cluster_score_post_injury_3 +
ocular_motor_cluster_score_post_injury_3 +
vestibular_cluster_score_post_injury_3 +
sleep_cluster_score_post_injury_3)
three_tests_cluster_scores_means <- three_tests_cluster_scores_join_all %>%
select(c(17:37)) %>%
summarize(mean_headache_score_1 = mean(headache_migraine_cluster_score_post_injury_1),
mean_headache_score_2 = mean(headache_migraine_cluster_score_post_injury_2),
mean_headache_score_3 = mean(headache_migraine_cluster_score_post_injury_3),
mean_cognitive_score_1 = mean(cognitive_cluster_score_post_injury_1),
mean_cognitive_score_2 = mean(cognitive_cluster_score_post_injury_2),
mean_cognitive_score_3 = mean(cognitive_cluster_score_post_injury_3),
mean_anxiety_score_1 = mean(anxiety_mood_cluster_score_post_injury_1),
mean_anxiety_score_2 = mean(anxiety_mood_cluster_score_post_injury_2),
mean_anxiety_score_3 = mean(anxiety_mood_cluster_score_post_injury_3),
mean_ocular_motor_score_1 = mean(ocular_motor_cluster_score_post_injury_1),
mean_ocular_motor_score_2 = mean(ocular_motor_cluster_score_post_injury_2),
mean_ocular_motor_score_3 = mean(ocular_motor_cluster_score_post_injury_3),
mean_vestibular_score_1 = mean(vestibular_cluster_score_post_injury_1),
mean_vestibular_score_2 = mean(vestibular_cluster_score_post_injury_2),
mean_vestibular_score_3 = mean(vestibular_cluster_score_post_injury_3),
mean_sleep_score_1 = mean(sleep_cluster_score_post_injury_1),
mean_sleep_score_2 = mean(sleep_cluster_score_post_injury_2),
mean_sleep_score_3 = mean(sleep_cluster_score_post_injury_3),
mean_total_score_1 = mean(total_symptom_score_post_injury_1),
mean_total_score_2 = mean(total_symptom_score_post_injury_2),
mean_total_score_3 = mean(total_symptom_score_post_injury_3)) %>%
mutate_if(is.numeric, ~round(., 2))
three_tests_cluster_scores_percent_decrease <- three_tests_cluster_scores_means %>%
mutate(perc_decrease_headache_score_1_2 = (mean_headache_score_1 - mean_headache_score_2) / mean_headache_score_1 * 100,
perc_decrease_headache_score_2_3 = (mean_headache_score_2 - mean_headache_score_3) / mean_headache_score_2 * 100,
perc_decrease_headache_score_1_3 = (mean_headache_score_1 - mean_headache_score_3) / mean_headache_score_1 * 100,
perc_decrease_cognitive_score_1_2 = (mean_cognitive_score_1 - mean_cognitive_score_2) / mean_cognitive_score_1 * 100,
perc_decrease_cognitive_score_2_3 = (mean_cognitive_score_2 - mean_cognitive_score_3) / mean_cognitive_score_2 * 100,
perc_decrease_cognitive_score_1_3 = (mean_cognitive_score_1 - mean_cognitive_score_3) / mean_cognitive_score_1 * 100,
perc_decrease_anxiety_score_1_2 = (mean_anxiety_score_1 - mean_anxiety_score_2) / mean_anxiety_score_1 * 100,
perc_decrease_anxiety_score_2_3 = (mean_anxiety_score_2 - mean_anxiety_score_3) / mean_anxiety_score_2 * 100,
perc_decrease_anxiety_score_1_3 = (mean_anxiety_score_1 - mean_anxiety_score_3) / mean_anxiety_score_1 * 100,
perc_decrease_ocular_motor_score_1_2 = (mean_ocular_motor_score_1 - mean_ocular_motor_score_2) / mean_ocular_motor_score_1 * 100,
perc_decrease_ocular_motor_score_2_3 = (mean_ocular_motor_score_2 - mean_ocular_motor_score_3) / mean_ocular_motor_score_2 * 100,
perc_decrease_ocular_motor_score_1_3 = (mean_ocular_motor_score_1 - mean_ocular_motor_score_3) / mean_ocular_motor_score_1 * 100,
perc_decrease_vestibular_score_1_2 = (mean_vestibular_score_1 - mean_vestibular_score_2) / mean_vestibular_score_1 * 100,
perc_decrease_vestibular_score_2_3 = (mean_vestibular_score_2 - mean_vestibular_score_3) / mean_vestibular_score_2 * 100,
perc_decrease_vestibular_score_1_3 = (mean_vestibular_score_1 - mean_vestibular_score_3) / mean_vestibular_score_1 * 100,
perc_decrease_sleep_score_1_2 = (mean_sleep_score_1 - mean_sleep_score_2) / mean_sleep_score_1 * 100,
perc_decrease_sleep_score_2_3 = (mean_sleep_score_2 - mean_sleep_score_3) / mean_sleep_score_2 * 100,
perc_decrease_sleep_score_1_3 = (mean_sleep_score_1 - mean_sleep_score_3) / mean_sleep_score_1 * 100,
perc_decrease_total_score_1_2 = (mean_total_score_1 - mean_total_score_2) / mean_total_score_1 * 100,
perc_decrease_total_score_2_3 = (mean_total_score_2 - mean_total_score_3) / mean_total_score_2 * 100,
perc_decrease_total_score_1_3 = (mean_total_score_1 - mean_total_score_3) / mean_total_score_1 * 100) %>%
mutate_if(is.numeric, ~round(., 2))
three_tests_cluster <- c("Headache-Migraine",
"Cognitive",
"Anxiety-Mood",
"Ocular-Motor",
"Vestibular",
"Sleep",
"Total Symptom Score")
three_tests_test_one_mean <- c(3.55, 3.68, 2.32, 0.45, 2.48, 3.62, 16.09)
three_tests_test_two_mean <- c(1.34, 1.39, 0.76, 0.13, 0.68, 1.37, 5.68)
three_tests_test_three_mean <- c(0.57, 0.63, 0.39, 0.06, 0.26, 0.59, 2.49)
three_tests_percent_decrease_1_2 <- c(.6225, .6223, .6724, .7111, .7258, .6215, .6470)
three_tests_percent_decrease_2_3 <- c(.5746, .5468, .4868, .5385, .6176, .5693, .5616)
three_tests_percent_decrease_1_3 <- c(.8394, .8288, .8319, .8667, .8952, .8370, .8452)
three_tests_percent_decrease_clean <- data_frame(three_tests_cluster,
three_tests_test_one_mean,
three_tests_test_two_mean,
three_tests_test_three_mean,
three_tests_percent_decrease_1_2,
three_tests_percent_decrease_2_3,
three_tests_percent_decrease_1_3)
three_tests_percent_decrease_tbl <- three_tests_percent_decrease_clean %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
three_tests_cluster = colDef(name = "Symptom Cluster"),
three_tests_test_one_mean = colDef(name = "Test One Mean"),
three_tests_test_two_mean = colDef(name = "Test Two Mean"),
three_tests_test_three_mean = colDef(name = "Test Three Mean"),
three_tests_percent_decrease_1_2 = colDef(name = "Tests 1 to 2 Percent Decrease",
format = colFormat(percent = TRUE)),
three_tests_percent_decrease_2_3 = colDef(name = "Tests 2 to 3 Percent Decrease",
format = colFormat(percent = TRUE)),
three_tests_percent_decrease_1_3 = colDef(name = "Tests 1 to 3 Percent Decrease",
format = colFormat(percent = TRUE))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
```
```{r, include=TRUE}
three_tests_percent_decrease_tbl
```
Row {.tabset}
-----------------------------------------------------------------------
### Four Tests Percent Decrease
```{r, include=FALSE}
head(clusters_four_impact_test_1)
head(clusters_four_impact_test_2)
head(clusters_four_impact_test_3)
head(clusters_four_impact_test_4)
four_tests_cluster_scores_join_1 <- left_join(clusters_four_impact_test_1,
clusters_four_impact_test_2)
four_tests_cluster_scores_join_2 <- left_join(four_tests_cluster_scores_join_1,
clusters_four_impact_test_3)
four_tests_cluster_scores_join_all <- left_join(four_tests_cluster_scores_join_2,
clusters_four_impact_test_4)
four_tests_cluster_scores_join_all <- four_tests_cluster_scores_join_all %>%
mutate(total_symptom_score_post_injury_1 = headache_migraine_cluster_score_post_injury_1 +
cognitive_cluster_score_post_injury_1 +
anxiety_mood_cluster_score_post_injury_1 +
ocular_motor_cluster_score_post_injury_1 +
vestibular_cluster_score_post_injury_1 +
sleep_cluster_score_post_injury_1,
total_symptom_score_post_injury_2 = headache_migraine_cluster_score_post_injury_2 +
cognitive_cluster_score_post_injury_2 +
anxiety_mood_cluster_score_post_injury_2 +
ocular_motor_cluster_score_post_injury_2 +
vestibular_cluster_score_post_injury_2 +
sleep_cluster_score_post_injury_2,
total_symptom_score_post_injury_3 = headache_migraine_cluster_score_post_injury_3 +
cognitive_cluster_score_post_injury_3 +
anxiety_mood_cluster_score_post_injury_3 +
ocular_motor_cluster_score_post_injury_3 +
vestibular_cluster_score_post_injury_3 +
sleep_cluster_score_post_injury_3,
total_symptom_score_post_injury_4 = headache_migraine_cluster_score_post_injury_4 +
cognitive_cluster_score_post_injury_4 +
anxiety_mood_cluster_score_post_injury_4 +
ocular_motor_cluster_score_post_injury_4 +
vestibular_cluster_score_post_injury_4 +
sleep_cluster_score_post_injury_4)
four_tests_cluster_scores_means <- four_tests_cluster_scores_join_all %>%
select(c(21:48)) %>%
summarize(mean_headache_score_1 = mean(headache_migraine_cluster_score_post_injury_1),
mean_headache_score_2 = mean(headache_migraine_cluster_score_post_injury_2),
mean_headache_score_3 = mean(headache_migraine_cluster_score_post_injury_3),
mean_headache_score_4 = mean(headache_migraine_cluster_score_post_injury_4),
mean_cognitive_score_1 = mean(cognitive_cluster_score_post_injury_1),
mean_cognitive_score_2 = mean(cognitive_cluster_score_post_injury_2),
mean_cognitive_score_3 = mean(cognitive_cluster_score_post_injury_3),
mean_cognitive_score_4 = mean(cognitive_cluster_score_post_injury_4),
mean_anxiety_score_1 = mean(anxiety_mood_cluster_score_post_injury_1),
mean_anxiety_score_2 = mean(anxiety_mood_cluster_score_post_injury_2),
mean_anxiety_score_3 = mean(anxiety_mood_cluster_score_post_injury_3),
mean_anxiety_score_4 = mean(anxiety_mood_cluster_score_post_injury_4),
mean_ocular_motor_score_1 = mean(ocular_motor_cluster_score_post_injury_1),
mean_ocular_motor_score_2 = mean(ocular_motor_cluster_score_post_injury_2),
mean_ocular_motor_score_3 = mean(ocular_motor_cluster_score_post_injury_3),
mean_ocular_motor_score_4 = mean(ocular_motor_cluster_score_post_injury_4),
mean_vestibular_score_1 = mean(vestibular_cluster_score_post_injury_1),
mean_vestibular_score_2 = mean(vestibular_cluster_score_post_injury_2),
mean_vestibular_score_3 = mean(vestibular_cluster_score_post_injury_3),
mean_vestibular_score_4 = mean(vestibular_cluster_score_post_injury_4),
mean_sleep_score_1 = mean(sleep_cluster_score_post_injury_1),
mean_sleep_score_2 = mean(sleep_cluster_score_post_injury_2),
mean_sleep_score_3 = mean(sleep_cluster_score_post_injury_3),
mean_sleep_score_4 = mean(sleep_cluster_score_post_injury_4),
mean_total_score_1 = mean(total_symptom_score_post_injury_1),
mean_total_score_2 = mean(total_symptom_score_post_injury_2),
mean_total_score_3 = mean(total_symptom_score_post_injury_3),
mean_total_score_4 = mean(total_symptom_score_post_injury_4)) %>%
mutate_if(is.numeric, ~round(., 2))
four_tests_cluster_scores_percent_decrease <- four_tests_cluster_scores_means %>%
mutate(perc_decrease_headache_score_1_2 = (mean_headache_score_1 - mean_headache_score_2) / mean_headache_score_1 * 100,
perc_decrease_headache_score_2_3 = (mean_headache_score_2 - mean_headache_score_3) / mean_headache_score_2 * 100,
perc_decrease_headache_score_3_4 = (mean_headache_score_3 - mean_headache_score_4) / mean_headache_score_3 * 100,
perc_decrease_headache_score_1_4 = (mean_headache_score_1 - mean_headache_score_4) / mean_headache_score_1 * 100,
perc_decrease_cognitive_score_1_2 = (mean_cognitive_score_1 - mean_cognitive_score_2) / mean_cognitive_score_1 * 100,
perc_decrease_cognitive_score_2_3 = (mean_cognitive_score_2 - mean_cognitive_score_3) / mean_cognitive_score_2 * 100,
perc_decrease_cognitive_score_3_4 = (mean_cognitive_score_3 - mean_cognitive_score_4) / mean_cognitive_score_3 * 100,
perc_decrease_cognitive_score_1_4 = (mean_cognitive_score_1 - mean_cognitive_score_4) / mean_cognitive_score_1 * 100,
perc_decrease_anxiety_score_1_2 = (mean_anxiety_score_1 - mean_anxiety_score_2) / mean_anxiety_score_1 * 100,
perc_decrease_anxiety_score_2_3 = (mean_anxiety_score_2 - mean_anxiety_score_3) / mean_anxiety_score_2 * 100,
perc_decrease_anxiety_score_3_4 = (mean_anxiety_score_3 - mean_anxiety_score_4) / mean_anxiety_score_3 * 100,
perc_decrease_anxiety_score_1_4 = (mean_anxiety_score_1 - mean_anxiety_score_4) / mean_anxiety_score_1 * 100,
perc_decrease_ocular_motor_score_1_2 = (mean_ocular_motor_score_1 - mean_ocular_motor_score_2) / mean_ocular_motor_score_1 * 100,
perc_decrease_ocular_motor_score_2_3 = (mean_ocular_motor_score_2 - mean_ocular_motor_score_3) / mean_ocular_motor_score_2 * 100,
perc_decrease_ocular_motor_score_3_4 = (mean_ocular_motor_score_3 - mean_ocular_motor_score_4) / mean_ocular_motor_score_3 * 100,
perc_decrease_ocular_motor_score_1_4 = (mean_ocular_motor_score_1 - mean_ocular_motor_score_4) / mean_ocular_motor_score_1 * 100,
perc_decrease_vestibular_score_1_2 = (mean_vestibular_score_1 - mean_vestibular_score_2) / mean_vestibular_score_1 * 100,
perc_decrease_vestibular_score_2_3 = (mean_vestibular_score_2 - mean_vestibular_score_3) / mean_vestibular_score_2 * 100,
perc_decrease_vestibular_score_3_4 = (mean_vestibular_score_3 - mean_vestibular_score_4) / mean_vestibular_score_3 * 100,
perc_decrease_vestibular_score_1_4 = (mean_vestibular_score_1 - mean_vestibular_score_4) / mean_vestibular_score_1 * 100,
perc_decrease_sleep_score_1_2 = (mean_sleep_score_1 - mean_sleep_score_2) / mean_sleep_score_1 * 100,
perc_decrease_sleep_score_2_3 = (mean_sleep_score_2 - mean_sleep_score_3) / mean_sleep_score_2 * 100,
perc_decrease_sleep_score_3_4 = (mean_sleep_score_3 - mean_sleep_score_4) / mean_sleep_score_3 * 100,
perc_decrease_sleep_score_1_4 = (mean_sleep_score_1 - mean_sleep_score_4) / mean_sleep_score_1 * 100,
perc_decrease_total_score_1_2 = (mean_total_score_1 - mean_total_score_2) / mean_total_score_1 * 100,
perc_decrease_total_score_2_3 = (mean_total_score_2 - mean_total_score_3) / mean_total_score_2 * 100,
perc_decrease_total_score_3_4 = (mean_total_score_3 - mean_total_score_4) / mean_total_score_3 * 100,
perc_decrease_total_score_1_4 = (mean_total_score_1 - mean_total_score_4) / mean_total_score_1 * 100) %>%
mutate_if(is.numeric, ~round(., 2))
four_tests_cluster <- c("Headache-Migraine",
"Cognitive",
"Anxiety-Mood",
"Ocular-Motor",
"Vestibular",
"Sleep",
"Total Symptom Score")
four_tests_test_one_mean <- c(4.04, 4.31, 2.70, 0.56, 3.06, 4.28, 18.95)
four_tests_test_two_mean <- c(1.83, 2.08, 1.07, 0.18, 1.03, 1.98, 8.17)
four_tests_test_three_mean <- c(0.99, 1.12, 0.63, 0.09, 0.48, 0.98, 4.28)
four_tests_test_four_mean <- c(0.60, 0.68, 0.43, 0.07, 0.29, 0.54, 2.62)
four_tests_percent_decrease_1_2 <- c(.5470, .5174, .6037, .6786, .6634, .5374, .5689)
four_tests_percent_decrease_2_3 <- c(.4590, .4615, .4112, .5000, .5340, .5051, .4761)
four_tests_percent_decrease_3_4 <- c(.3939, .3929, .3175, .2222, .3958, .4490, .3879)
four_tests_percent_decrease_1_4 <- c(.8515, .8422, .8407, .8750, .9052, .8738, .8617)
four_tests_percent_decrease_clean <- data_frame(four_tests_cluster,
four_tests_test_one_mean,
four_tests_test_two_mean,
four_tests_test_three_mean,
four_tests_test_four_mean,
four_tests_percent_decrease_1_2,
four_tests_percent_decrease_2_3,
four_tests_percent_decrease_3_4,
four_tests_percent_decrease_1_4)
four_tests_percent_decrease_tbl <- four_tests_percent_decrease_clean %>%
reactable(
defaultColDef = colDef(align = "center"),
columns = list(
four_tests_cluster = colDef(name = "Symptom Cluster"),
four_tests_test_one_mean = colDef(name = "Test One Mean"),
four_tests_test_two_mean = colDef(name = "Test Two Mean"),
four_tests_test_three_mean = colDef(name = "Test Three Mean"),
four_tests_test_four_mean = colDef(name = "Test Four Mean"),
four_tests_percent_decrease_1_2 = colDef(name = "Tests 1 to 2 Percent Decrease",
format = colFormat(percent = TRUE)),
four_tests_percent_decrease_2_3 = colDef(name = "Tests 2 to 3 Percent Decrease",
format = colFormat(percent = TRUE)),
four_tests_percent_decrease_3_4 = colDef(name = "Tests 3 to 4 Percent Decrease",
format = colFormat(percent = TRUE)),
four_tests_percent_decrease_1_4 = colDef(name = "Tests 1 to 4 Percent Decrease",
format = colFormat(percent = TRUE))),
pagination = TRUE,
striped = TRUE,
outlined = TRUE,
compact = TRUE,
highlight = TRUE,
bordered = TRUE
)
```
```{r, include=TRUE}
four_tests_percent_decrease_tbl
```
Row {.tabset}
-----------------------------------------------------------------------
### Two Tests Non-Normalized Test 1 Summary
```{r, include=FALSE}
clusters_two_impact_test_1
two_clusters_test_1 <- clusters_two_impact_test_1 %>%
pivot_longer(
cols = c(13:18),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(two_clusters_test_1,
gender,
symptom_cluster,
post_injury_1_score)
```
### Two Tests Non-Normalized Test 2 Summary
```{r, include=FALSE}
two_clusters_test_2 <- clusters_two_impact_test_2 %>%
pivot_longer(
cols = c(13:18),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(two_clusters_test_2,
gender,
symptom_cluster,
post_injury_2_score)
```
Row {.tabset}
-----------------------------------------------------------------------
### Three Tests Non-Normalized Test 1 Summary
```{r, include=FALSE}
three_clusters_test_1 <- clusters_three_impact_test_1 %>%
pivot_longer(
cols = c(17:22),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(three_clusters_test_1,
gender,
symptom_cluster,
post_injury_1_score)
```
### Three Tests Non-Normalized Test 2 Summary
```{r, include=FALSE}
three_clusters_test_2 <- clusters_three_impact_test_2 %>%
pivot_longer(
cols = c(17:22),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(three_clusters_test_2,
gender,
symptom_cluster,
post_injury_2_score)
```
### Three Tests Non-Normalized Test 3 Summary
```{r, include=FALSE}
three_clusters_test_3 <- clusters_three_impact_test_3 %>%
pivot_longer(
cols = c(17:22),
names_to = "symptom_cluster",
values_to = "post_injury_3_score",
names_pattern = "(.*)_score_post_injury_3"
)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(three_clusters_test_3,
gender,
symptom_cluster,
post_injury_3_score)
```
Row {.tabset}
-----------------------------------------------------------------------
### Four Tests Non-Normalized Test 1 Summary
```{r, include=FALSE}
four_clusters_test_1 <- clusters_four_impact_test_1 %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_1_score",
names_pattern = "(.*)_score_post_injury_1"
)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(four_clusters_test_1,
gender,
symptom_cluster,
post_injury_1_score)
```
### Four Tests Non-Normalized Test 2 Summary
```{r, include=FALSE}
four_clusters_test_2 <- clusters_four_impact_test_2 %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_2_score",
names_pattern = "(.*)_score_post_injury_2"
)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(four_clusters_test_2,
gender,
symptom_cluster,
post_injury_2_score)
```
### Four Tests Non-Normalized Test 3 Summary
```{r, include=FALSE}
four_clusters_test_3 <- clusters_four_impact_test_3 %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_3_score",
names_pattern = "(.*)_score_post_injury_3"
)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(four_clusters_test_3,
gender,
symptom_cluster,
post_injury_3_score)
```
### Four Tests Non-Normalized Test 4 Summary
```{r, include=FALSE}
four_clusters_test_4 <- clusters_four_impact_test_4 %>%
pivot_longer(
cols = c(21:26),
names_to = "symptom_cluster",
values_to = "post_injury_4_score",
names_pattern = "(.*)_score_post_injury_4"
)
```
```{r, include=TRUE}
scaled_cluster_gender_tbl(four_clusters_test_4,
gender,
symptom_cluster,
post_injury_4_score)
```
